Periodic Reporting for period 1 - HIYIELD (Highly efficient technologies for increased yields in steelmaking processes and reduced environmental impact)
Reporting period: 2022-07-01 to 2023-12-31
The steel industry is responsible for around 7 % of global CO2 emissions. Therefore, the sector’s decarbonization will play a key role in achieving the EU climate goals for 2050. Scrap steel can make an important contribution. Increasing the use of scrap to minimize pig iron (hot metal) usage and reducing iron ore in coal-fired blast furnaces to reduce CO2 emissions is a method to achieve more sustainable and competitive production. Furthermore, retaining alloying elements in scrap steel will valorize scrap. The EU-funded HIYIELD project will maximize scrap quality by improving technologies for the removal of impurities and optimizing use of alloying elements. The approach includes improved scrap identification and classification with tracking in the circular economy. The main industrial objectives, which the project addresses, beside many others, are:
• To maximize scrap quality by optimal technologies for impurities removal and optimal use of alloying elements.
• To maximize scrap use by improved scrap identification and classification together with scrap tracking in circular economy.
• To maximize product quality with further scrap uptake by charge optimization and ensuring the liquid steel analysis and thus the final steel product quality.
The targeted topic implements the co-programmed European Clean Steel Partnership (CSP), developed in the context of the EU goal and policies to achieve climate neutrality by 2050. Global crude steel production reached 1,864 million tons (Mt) in 2020. Second producer after Asia (i.e. China), the EU produced 138.8 Mt of crude steel in 2020, very substantial numbers in spite of the Covid-19 pandemic. On average, 1.8 tons of CO2 are emitted for every ton of steel produced. Overall, the iron and steel industry accounts for approximately 7% of total world CO2 emissions, directly accounting for 2.6 Gt CO2 and ranking first when it comes to CO2 emissions, and second when it comes to energy consumption. HIYIELD represents the effort of selected key representatives of the steelmaking value chain (steelmakers, scrap suppliers, sampler & probe suppliers and technology partners) to contribute to the reduction of these emissions and thereby to the compliance of EU climate targets. HIYIELD targets the implementation of innovative ICT technologies and methods such as augmented reality, Artificial Intelligence, Machine Learning and Big Data aiming at increasing the scrap uptake in a number of scenarios that represent the current European steelmaking routes. The overarching objective is to increase the environmental sustainability and the competitive advantage of the European steel production value chain anticipating and addressing the emergent need of European producers for cleaner, source-controlled and better products. The consortium is properly balanced with the presence of steel manufacturers and scrap suppliers.
• To maximize scrap quality by optimal technologies for impurities removal and optimal use of alloying elements.
• To maximize scrap use by improved scrap identification and classification together with scrap tracking in circular economy.
• To maximize product quality with further scrap uptake by charge optimization and ensuring the liquid steel analysis and thus the final steel product quality.
The targeted topic implements the co-programmed European Clean Steel Partnership (CSP), developed in the context of the EU goal and policies to achieve climate neutrality by 2050. Global crude steel production reached 1,864 million tons (Mt) in 2020. Second producer after Asia (i.e. China), the EU produced 138.8 Mt of crude steel in 2020, very substantial numbers in spite of the Covid-19 pandemic. On average, 1.8 tons of CO2 are emitted for every ton of steel produced. Overall, the iron and steel industry accounts for approximately 7% of total world CO2 emissions, directly accounting for 2.6 Gt CO2 and ranking first when it comes to CO2 emissions, and second when it comes to energy consumption. HIYIELD represents the effort of selected key representatives of the steelmaking value chain (steelmakers, scrap suppliers, sampler & probe suppliers and technology partners) to contribute to the reduction of these emissions and thereby to the compliance of EU climate targets. HIYIELD targets the implementation of innovative ICT technologies and methods such as augmented reality, Artificial Intelligence, Machine Learning and Big Data aiming at increasing the scrap uptake in a number of scenarios that represent the current European steelmaking routes. The overarching objective is to increase the environmental sustainability and the competitive advantage of the European steel production value chain anticipating and addressing the emergent need of European producers for cleaner, source-controlled and better products. The consortium is properly balanced with the presence of steel manufacturers and scrap suppliers.
HIYIELD contributes to the fight against climate change and to the EU steel industry competitiveness through the application of highly innovative methods and technologies such as:
• Deep Learning based Computer Vision for scrap identification and control: Scrap upgrading will be followed by intelligent use including scrap validation before charging at steelmaking. In the steelmaking facility new sensors (laser scanners/hyperspectral camera) will be installed for retrieving scrap properties. Moreover, a deep learning based approach will be used both to automatically classify the scrap magnetized and to correlate scrap properties and process data for optimizing the scrap charge operation. A system for optimum use of new scrap grades will be implemented using smart data management tool. Suggestions for further standardization at European level will be elaborated.
• Digital Scrap Information Card for scrap tracking: The Digital Scrap Information Card should be a standardized communication interface between scrap dealers and steel manufacturer. The standard describes the functional and non-functional characteristics of the necessary information (e.g. scrap quality, scrap analysis, quantity, dates, etc.) exchange. The Digital Scrap Information Card should use de facto industry standards to connect devices from different vendors.
• High Speed Sampling and analysis to avoid waiting times for steel analysis: Within this project the current trends and indications of all the governing factors regarding optical emission spectroscopy (OES) techniques are taken into consideration and used as a guideline for the continuing work on improving the direct analysis type sampler system. This novel sampler system utilizes a direct on-site analysis of steel samples which could shorten the total steel sampling time.
• Deep Learning based Computer Vision for scrap identification and control: Scrap upgrading will be followed by intelligent use including scrap validation before charging at steelmaking. In the steelmaking facility new sensors (laser scanners/hyperspectral camera) will be installed for retrieving scrap properties. Moreover, a deep learning based approach will be used both to automatically classify the scrap magnetized and to correlate scrap properties and process data for optimizing the scrap charge operation. A system for optimum use of new scrap grades will be implemented using smart data management tool. Suggestions for further standardization at European level will be elaborated.
• Digital Scrap Information Card for scrap tracking: The Digital Scrap Information Card should be a standardized communication interface between scrap dealers and steel manufacturer. The standard describes the functional and non-functional characteristics of the necessary information (e.g. scrap quality, scrap analysis, quantity, dates, etc.) exchange. The Digital Scrap Information Card should use de facto industry standards to connect devices from different vendors.
• High Speed Sampling and analysis to avoid waiting times for steel analysis: Within this project the current trends and indications of all the governing factors regarding optical emission spectroscopy (OES) techniques are taken into consideration and used as a guideline for the continuing work on improving the direct analysis type sampler system. This novel sampler system utilizes a direct on-site analysis of steel samples which could shorten the total steel sampling time.
The concept of the project is based on a preparation phase and a validation phase. In the preparation phase, the challenges will be mapped in detail and the required tools will be implemented simultaneously. Afterwards, they are applied in the three demo cases.
Demonstration case 1: A system for optimum use of new scrap grades will be implemented using smart data management tool. Suggestions for further standardization at European level will be elaborated.
Demonstration case 2: A system to automatize internal and external scrap routines, integrating them into the ambient heterogeneous infrastructure, develop refined & performant tools for various dimensions of scrap analysis at different steps in the scrap process. In addition this system will provide a standardized exchange format and interface between supplier and costumer for a Scrap Information Card containing all relevant information associated to the respective scrap batch.
Demonstration case 3: A system to implement a steel sampling and analyzing technique for a direct on-site analysis of steel samples without additional waiting time including direct transfer of the result by modern IoT communication technology. Reducing the overall time for liquid steel analysis to less than 5 minutes, will enable steelmakers reduce treatment time and energy costs. Furthermore, liquid steel quality can be quickly measured, so that countermeasures are still possible.
Demonstration case 1: A system for optimum use of new scrap grades will be implemented using smart data management tool. Suggestions for further standardization at European level will be elaborated.
Demonstration case 2: A system to automatize internal and external scrap routines, integrating them into the ambient heterogeneous infrastructure, develop refined & performant tools for various dimensions of scrap analysis at different steps in the scrap process. In addition this system will provide a standardized exchange format and interface between supplier and costumer for a Scrap Information Card containing all relevant information associated to the respective scrap batch.
Demonstration case 3: A system to implement a steel sampling and analyzing technique for a direct on-site analysis of steel samples without additional waiting time including direct transfer of the result by modern IoT communication technology. Reducing the overall time for liquid steel analysis to less than 5 minutes, will enable steelmakers reduce treatment time and energy costs. Furthermore, liquid steel quality can be quickly measured, so that countermeasures are still possible.